C4.5: programs for machine learning
C4.5: programs for machine learning
Brain-computer interface: a new communication device for handicapped persons
Journal of Microcomputer Applications - Special issue on computer applications for handicapped persons
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Classification of hazelnut kernels by using impact acoustic time-frequency patterns
EURASIP Journal on Advances in Signal Processing
Pattern Recognition
Computers in Biology and Medicine
Computers in Biology and Medicine
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We introduce a new adaptive time-frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain-computer interface task. The proposed algorithm adaptively segments the time axis by dividing the EEG data into non-uniform time segments over a dyadic tree. This is followed by grouping the expansion coefficients in the frequency axis in each segment. The most discriminative features are selected from the segmented time-frequency plane and fed to a linear discriminant for classification. The proposed algorithm achieved an average classification accuracy of 84.3% on six subjects by selecting the most discriminant subspaces for each one. For comparison, classification results based on an autoregressive model are also presented where the mean accuracy of the same subjects turned out to be 79.5%. Interestingly the subjects and two hemispheres of each subject are represented by distinct segmentations and features. This indicates that the proposed method can handle inter-subject variability when constructing brain-computer interfaces.